Build AI powered apps for your work

Get started free
LLM ComparisonGPT-5 NanoGPT-4o

GPT-5 Nano vs GPT-4o

Compare GPT-5 Nano and GPT-4o. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5 NanoGPT-4o
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens128,000 tokens
Input Cost
$0.05/ 1M tokens
$2.50/ 1M tokens
Output Cost
$0.40/ 1M tokens
$10.00/ 1M tokens

Stop choosing. Use both.

With Appaca you don't have to pick — build apps that are powered by GPT-5 Nano, GPT-4o, for your specific use case.

Build your first app free

Strengths & Best Use Cases

GPT-5 Nano

OpenAI

1. Extremely fast performance

  • Fastest model in the GPT-5 family.
  • Great for real-time workflows, rapid responses, and high-throughput systems.

2. Most cost-efficient GPT-5 model

  • Lowest input and output token costs.
  • Suitable for large-scale or budget-sensitive applications.

3. Ideal for lightweight, well-scoped tasks

  • Excels at summarization, classification, text extraction, and simple logic tasks.
  • Best used when tasks are narrow and well-defined.

4. Multimodal input

  • Accepts text + image as input.
  • Outputs text only.

5. Broad tool support

  • Supports Web Search, File Search, Image Generation (as a tool), Code Interpreter, and MCP.
  • (Does not support Computer Use.)

GPT-4o

OpenAI

1. High-intelligence, general-purpose model

  • Strong reasoning, creativity, summarization, and problem-solving.
  • Great balance of speed, accuracy, and cost.

2. Multimodal input support

  • Accepts text + image inputs for visual reasoning, extraction, or description.
  • Output is text only, making it predictable for production.

3. Excellent for structured and unstructured tasks

  • Performs well on Q&A, writing, analysis, classification, chat, and planning.
  • Supports Structured Outputs, making it suitable for deterministic workflows.

4. Strong tool-use capabilities

  • Supports function calling, API orchestration, and tool-augmented workflows.
  • Integrates well with assistants, batch operations, and automation pipelines.

5. Large context for complex tasks

  • 128K context allows multi-document reasoning, multi-step conversations, and large input payloads.

6. Production-ready reliability

  • Stable outputs, predictable behaviors, and broad modality coverage.
  • Supported across all major API endpoints.

7. Lower latency than o-series reasoning models

  • Faster responses due to no dedicated reasoning step.
  • Ideal for interactive or near-real-time applications.

8. Fine-tuning and distillation supported

  • Enables specialization for domain-specific tasks.
  • Distillation helps create smaller, efficient custom models.